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Re: [NMusers] ETAs & SIGMA in external validation

From: Jakob Ribbing <>
Date: Sat, 7 Apr 2018 07:24:06 +0200

Hi Tingjie,

It does not: Sigma squared is the sum of all error variances, and assay =
error in most cases is only a small contribution to this sum.
There are exceptions, but when applying a previous model to new data it =
is rarely the first modification that comes to my mind.

Given your objectives with the model, maybe the best evaluation would be =
to obtain individual parameters based on subject’s first visit =
(IGNORE later time points in $DATA), and then see how well these etas =
predict the DV at subsequent visits?
To split by visit is only an example, obviously, if your project is in =
anesthesia it may be too late for dose adjustment after the visit is =
over, and in other cases observations over several days or weeks may be =
more relevant, for prediction of even later points in time.
Best regards


> On 6 Apr 2018, at 21:40, Tingjie Guo <> wrote:
> Small correction in question 2: SIGMA (instead of OMEGA) value =
influences individual ETAs...
> _at_Leonid, _at_Jakob, Thank you both for your input.
> _at_Jakob, You are right, I'm interested in individual ETAs. The idea is =
to evaluate the predictive ability of the model in particular subjects =
(external data) in order to guide clinical care for these subjects. Does =
this purpose alter your opinion on SIGMA choice?
> Yours sincerely,
> Tingjie Guo
> On Fri, Apr 6, 2018 at 7:51 PM, Leonid Gibiansky =
< <>> wrote:
> It would be better to use
> (at least if the original model was fit with INTERACTION option and =
residual error model is not additive).
> One option is to use Para = THETA * EXP(ETA)
> You would be changing the model, but the model is not too good any way =
if you need to restrict Para > 0 artificially.
> SIGMA should be taken from the model.
> Leonid
> On 4/6/2018 12:32 PM, Tingjie Guo wrote:
> Dear NMusers,
> I have two questions regarding the statistical model when performing =
external validation. I have a dataset and would like to validate a =
published model with POSTHOC method i.e. $EST METHOD=0 POSTHOC =
> 1. The model added etas in proportional way, i.e. Para = THETA * =
(1+ETA) and this made the posthoc estimation fail due to the negative =
individual parameter estimate in some subjects. I constrained it to be =
positive by adding ABS function i.e. Para = THETA * ABS(1+ETA), and =
the estimation can be successfully running. I was wondering if there is =
better workaround?
> 2. OMEGA value influences individual ETAs in POSTHOC estimation. =
Should we assign $SIGMA with model value or lab (where external data was =
determined) assay error value? If we use model value, it's =
understandable that $SIGMA contains unexplained variability and thus it =
is a part of the model. However, I may also understand it as that model =
value contains the unexplained variability for original data (in which =
the model was created) but not for external data. I'm a little confused =
about it. Can someone help me out?
> I would appreciate any response! Many thanks in advance!
> Your sincerely,
> Tingjie Guo

Received on Sat Apr 07 2018 - 01:24:06 EDT

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